用于电路级缺陷预测的高效机器学习辅助故障分析方法

Joydeep Ghosh
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引用次数: 0

摘要

准确使用故障分析(FA)是晶体管技术进步取得成功的关键,它有利于微调和优化制造工艺。然而,随着器件尺寸、结构和材料复杂性的急剧扩大,芯片制造商面临着多项故障分析挑战。为了保持可制造性,可以在芯片加工和设计的所有步骤中加快缺陷识别。另一方面,随着技术扩展到纳米节点以下,器件对不可避免的工艺引起的变异更加敏感。因此,在芯片扩展过程中,需要同时处理金属缺陷和工艺引起的变异性,并开发故障诊断方法,将两者的影响分离开来。事实上,在存在变异性的情况下,要从微芯片中成千上万的电路中找出有缺陷的元件是一项繁琐的任务。这项工作展示了如何有效利用 SPICE 电路仿真和基于机器学习的物理建模来解决 6T-SRAM 位单元的这一问题。通过在仿真数据上训练一个预测模型,为这种电路设计了一个自动桥接缺陷识别系统。对于模型的特征描述,利用了电路的对称性和基本材料特性:金属(半导体)在一定电压范围内具有正(负)电阻温度系数。然后,这项工作成功证明了如何以约 99.5% 的准确率识别出故障电路及其故障元件的位置。这一建议的解决方案将大大有助于加快集成电路的生产过程。
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Efficient machine learning-assisted failure analysis method for circuit-level defect prediction

Integral to the success of transistor advancements is the accurate use of failure analysis (FA) which benefits in fine-tuning and optimization of the fabrication processes. However, the chip makers face several FA challenges as device sizes, structure, and material complexities scale dramatically. To sustain manufacturability, one can accelerate defect identification at all steps of the chip processing and design. On the other hand, as technologies scale below the nanometer nodes, devices are more sensitive to unavoidable process-induced variability. Therefore, metallic defects and process-induced variability need to be treated concurrently in the context of chip scaling, while failure diagnostic methods to decouple the effects should be developed. Indeed, the locating a defective component from thousands of circuits in a microchip in the presence of variability is a tedious task. This work shows how the SPICE circuit simulations coupled with machine learning based-physical modeling should be effectively used to tackle such a problem for a 6T-SRAM bit cell. An automatic bridge defect recognition system for such a circuit is devised by training a predictive model on simulation data. For feature descriptors of the model, the symmetry of the circuit and a fundamental material property are leveraged: metals (semiconductors) have a positive (negative) temperature coefficient of resistance up to a certain voltage range. Then, this work successfully demonstrates that how a defective circuit is identified along with its defective component's position with approximately 99.5 % accuracy. This proposed solution should greatly help to accelerate the production process of the integrated circuits.

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Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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